I don't understand the technology enough to see why, but LLMs seem unusually skilled at reverse engineering proprietary software.

▲ mmh0000 4 hours ago | parent | next [-]

LLMs are good at producing what they/the public know.

In this case:

  LLMs know the USB Spec very well.

  LLMs know how to read raw packet dumps.

  LLMs know how to convert a packet dump to USB spec

  LLMs know how to write code to generate USB packets from the spec.

LLMs are also VERY good at transliteration, i.e., converting known-good Python to Rust.

Basically, If you have a well-documented problem, the LLM is a shortcut to learning it yourself. LLMs fail when you have a novel or poorly documented problem. They also fail when you provide the LLM with terrible context or too much context.

	▲	CamperBob2 2 hours ago \| parent [-]
		Don't sell in-context learning short. Right now I'm waiting on Claude to wrap up the latest of a half-dozen extensive changes to XML files for a fairly-obscure (and obsolete) closed-source electronics CAD program. I am pretty sure it doesn't know anything about these files besides what's in the XML .DTD file (which I also gave it.) This is a very novel, reasonably-poorly-documented problem, and so far it has batted 1.000.

▲ danielheath 4 hours ago | parent | prev | next [-]

IMO it's because they don't get burnt out by a lack of results.

After 5-6 consecutive approaches fail, I need a reason to think the next one might work out to stay motivated.

Claude will keep burning credits trying new approaches until something sticks. That's a huge advantage in a field where most of the things you try don't go anywhere.

▲ notfromhere 4 hours ago | parent | prev | next [-]

LLMs seem to be trained to work very well against a goal, especially one it can verify against. I guess because it can easily know if it passed or failed, va other tasks where good/bad output is subjective

▲ jasonfarnon 2 hours ago | parent | prev | next [-]

It's LLMs' generalist skills. I think reverse engineering is usually hard when you're in an unfamiliar domain. Eg I've never programmed a videogame or windows application but I'm trying to crack one. On the other hand if you know the domain, and know what the programmers' intentions must be in any given block of code, it's often straightforward. There are no unfamiliar domains for LLMs, including it seems proprietary software.

▲ fl0id 4 hours ago | parent | prev | next [-]

To some degree. But esp for drivers, you still need to know when they go wrong, and steer them right, or your code will either just not work or be an unmaintainable, not-upstreamable mess.

▲ IshKebab 4 hours ago | parent | prev [-]

I don't think they're any more skilled at it than someone who knows how to reverse engineer stuff... But it is definitely a place where AI is amazing because reverse engineering is usually extremely time consuming and tedious. AI doesn't care about that.

It also has the benefit that it doesn't usually matter too much if it gets minor details wrong. It's definitely one of the areas - like hacking - where it's a) tedious and b) insensitive to mistakes where AI absolutely shines.

	▲	mjg59 3 hours ago \| parent \| next [-]
		My experience is that they're better than me at a lot of the process, so probably worse than someone who's a full time reverse engineer but as good as or better than most. They'll definitely get some small details wrong that would derail the entire thing, so having some skills that are pretty much "This smells wrong" helps a lot, but I think for many scenarios they'll unblock someone who has little RE experience.
	▲	SchemaLoad 2 hours ago \| parent \| prev [-]
		Also verifiable. LLMs shine when they can know when the task is completed correctly. Otherwise they will finish and hand over something that's wrong.